Network embedding, which aims to embed each node into a low dimensional vector by preserving the network structure as much as possible, has been widely used to solve many problems in network science, e.g., link prediction [1]}, [2]}, community detection [3]}, [4]}, [5]} and network reconstruction [6]}, [7]}, [8]}. In this paper, we further widen the application of network embedding, i.e., we explore how to use network embedding to characterize the dissimilarity of two networks in a state-of-the-art way. We start from using a simple and fast network embedding algorithm, i.e., DeepWalk, to measure the distance between two nodes. The information content of a network, i.e., network similarity heterogeneity, is defined based on the node distance distribution and Jensen-Shannon divergence. Accordingly, the dissimilarity between two networks is further defined upon network similarity heterogeneity. We validate the effectiveness of the embedding based network comparison method on both synthetic and empirical networks. Compared to the baseline methods, embedding based network comparison shows high distinguishability.
